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SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions

MOTIVATION: Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we in...

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Autores principales: Nishizaki, Sierra S., Boyle, Alan P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351228/
https://www.ncbi.nlm.nih.gov/pubmed/35927613
http://dx.doi.org/10.1186/s12859-022-04865-x
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author Nishizaki, Sierra S.
Boyle, Alan P.
author_facet Nishizaki, Sierra S.
Boyle, Alan P.
author_sort Nishizaki, Sierra S.
collection PubMed
description MOTIVATION: Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factor’s motif. RESULTS: SEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions for CTCF. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease. AVAILABILITY AND IMPLEMENTATION: SEMplMe is available from https://github.com/Boyle-Lab/SEMplMe. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04865-x.
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spelling pubmed-93512282022-08-05 SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions Nishizaki, Sierra S. Boyle, Alan P. BMC Bioinformatics Software MOTIVATION: Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factor’s motif. RESULTS: SEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions for CTCF. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease. AVAILABILITY AND IMPLEMENTATION: SEMplMe is available from https://github.com/Boyle-Lab/SEMplMe. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04865-x. BioMed Central 2022-08-04 /pmc/articles/PMC9351228/ /pubmed/35927613 http://dx.doi.org/10.1186/s12859-022-04865-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Nishizaki, Sierra S.
Boyle, Alan P.
SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions
title SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions
title_full SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions
title_fullStr SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions
title_full_unstemmed SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions
title_short SEMplMe: a tool for integrating DNA methylation effects in transcription factor binding affinity predictions
title_sort semplme: a tool for integrating dna methylation effects in transcription factor binding affinity predictions
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351228/
https://www.ncbi.nlm.nih.gov/pubmed/35927613
http://dx.doi.org/10.1186/s12859-022-04865-x
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